weather_df =
rnoaa::meteo_pull_monitors(c("USW00094728", "USC00519397", "USS0023B17S"),
var = c("PRCP", "TMIN", "TMAX"),
date_min = "2017-01-01",
date_max = "2017-12-31") %>%
mutate(
name = recode(id, USW00094728 = "CentralPark_NY",
USC00519397 = "Waikiki_HA",
USS0023B17S = "Waterhole_WA"),
tmin = tmin / 10,
tmax = tmax / 10) %>%
select(name, id, everything())
weather_df
## # A tibble: 1,095 x 6
## name id date prcp tmax tmin
## <chr> <chr> <date> <dbl> <dbl> <dbl>
## 1 CentralPark_NY USW00094728 2017-01-01 0 8.9 4.4
## 2 CentralPark_NY USW00094728 2017-01-02 53 5 2.8
## 3 CentralPark_NY USW00094728 2017-01-03 147 6.1 3.9
## 4 CentralPark_NY USW00094728 2017-01-04 0 11.1 1.1
## 5 CentralPark_NY USW00094728 2017-01-05 0 1.1 -2.7
## 6 CentralPark_NY USW00094728 2017-01-06 13 0.6 -3.8
## 7 CentralPark_NY USW00094728 2017-01-07 81 -3.2 -6.6
## 8 CentralPark_NY USW00094728 2017-01-08 0 -3.8 -8.8
## 9 CentralPark_NY USW00094728 2017-01-09 0 -4.9 -9.9
## 10 CentralPark_NY USW00094728 2017-01-10 0 7.8 -6
## # ... with 1,085 more rows
Adding axis labels and titles
ggplot(weather_df, aes(x = tmin, y = tmax)) +
geom_point(aes(color = name), alpha = .5) +
labs(
title = "Temperature plot",
x = "Minimum daily temperature (C)",
y = "Maxiumum daily temperature (C)",
caption = "Data from the rnoaa package"
)
## Warning: Removed 15 rows containing missing values (geom_point).
Tick marks and labels… option + 0 = degrees symbolº
ggplot(weather_df, aes(x = tmin, y = tmax)) +
geom_point(aes(color = name), alpha = .5) +
labs(
title = "Temperature plot",
x = "Minimum daily temperature (ºC)",
y = "Maxiumum daily temperature (ºC)",
caption = "Data from the rnoaa package"
) +
scale_x_continuous(breaks = c(-15, 0, 15),
labels = c("-15", "0", "15"),
limits = c(-20, 32)
) +
scale_y_continuous(
position = "right",
trans = "sqrt"
)
## Warning in self$trans$transform(x): NaNs produced
## Warning: Transformation introduced infinite values in continuous y-axis
## Warning: Removed 90 rows containing missing values (geom_point).
Adjust color
ggplot(weather_df, aes(x = tmin, y = tmax)) +
geom_point(aes(color = name), alpha = .5) +
labs(
title = "Temperature plot",
x = "Minimum daily temperature (C)",
y = "Maxiumum daily temperature (C)",
caption = "Data from the rnoaa package"
) +
scale_color_hue(name = "Location",
h = c(100, 300),
l = 75)
## Warning: Removed 15 rows containing missing values (geom_point).
Viridis package
ggplot(weather_df, aes(x = tmin, y = tmax)) +
geom_point(aes(color = name), alpha = .5) +
labs(
title = "Temperature plot",
x = "Minimum daily temperature (C)",
y = "Maxiumum daily temperature (C)",
caption = "Data from the rnoaa package"
) +
viridis::scale_color_viridis(
name = "Location",
discrete = TRUE
)
## Warning: Removed 15 rows containing missing values (geom_point).
ggplot(weather_df, aes(x = tmin, y = tmax)) +
geom_point(aes(color = name), alpha = .5) +
labs(
title = "Temperature plot",
x = "Minimum daily temperature (C)",
y = "Maxiumum daily temperature (C)",
caption = "Data from the rnoaa package"
) +
viridis::scale_color_viridis(
name = "Location",
discrete = TRUE
) +
theme(legend.position = "bottom")
## Warning: Removed 15 rows containing missing values (geom_point).
ggplot(weather_df, aes(x = tmin, y = tmax)) +
geom_point(aes(color = name), alpha = .5) +
labs(
title = "Temperature plot",
x = "Minimum daily temperature (C)",
y = "Maxiumum daily temperature (C)",
caption = "Data from the rnoaa package"
) +
viridis::scale_color_viridis(
name = "Location",
discrete = TRUE
) +
theme_bw()
## Warning: Removed 15 rows containing missing values (geom_point).
ggplot(weather_df, aes(x = tmin, y = tmax)) +
geom_point(aes(color = name), alpha = .5) +
labs(
title = "Temperature plot",
x = "Minimum daily temperature (C)",
y = "Maxiumum daily temperature (C)",
caption = "Data from the rnoaa package"
) +
viridis::scale_color_viridis(
name = "Location",
discrete = TRUE
) +
theme_bw() +
theme(legend.position = "right")
## Warning: Removed 15 rows containing missing values (geom_point).
ggplot(weather_df, aes(x = tmin, y = tmax)) +
geom_point(aes(color = name), alpha = .5) +
labs(
title = "Temperature plot",
x = "Minimum daily temperature (C)",
y = "Maxiumum daily temperature (C)",
caption = "Data from the rnoaa package"
) +
viridis::scale_color_viridis(
name = "Location",
discrete = TRUE
)
## Warning: Removed 15 rows containing missing values (geom_point).
+ ggthemes::fivethirtyeight_pal() Learning assessment
ggplot(weather_df, aes(x = date, y = tmax, color = name)) +
geom_smooth(se = FALSE) +
geom_point(aes(size = prcp), alpha = .5) +
labs(
title = "Temperature plot",
x = "Date",
y = "Maximum daily temperature (Cº)",
caption = "Data from the rnoaa package"
) +
viridis::scale_color_viridis(
name = "Location",
discrete = TRUE
) +
theme_bw() +
theme(legend.position = "bottom")
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## Warning: Removed 3 rows containing non-finite values (stat_smooth).
## Warning: Removed 3 rows containing missing values (geom_point).
geom_*centralpark_df = weather_df %>% filter(name == "CentralPark_NY")
waikiki_df = weather_df %>% filter(name == "Waikiki_HA")
ggplot(waikiki_df, aes(x = date, y = tmax, color = name)) +
geom_point() +
geom_line(data = centralpark_df)
## Warning: Removed 3 rows containing missing values (geom_point).
ggplot(weather_df, aes(x = date, y = tmax, color = name)) +
geom_smooth(se = FALSE) +
geom_point(aes(size = prcp), alpha = .5) +
facet_grid(~name) +
labs(
title = "Temperature plot",
x = "Date",
y = "Maximum daily temperature (Cº)",
caption = "Data from the rnoaa package"
) +
viridis::scale_color_viridis(
name = "Location",
discrete = TRUE
) +
theme_bw() +
theme(legend.position = "bottom")
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## Warning: Removed 3 rows containing non-finite values (stat_smooth).
## Warning: Removed 3 rows containing missing values (geom_point).
devtools::install_github("thomasp85/patchwork")
## Skipping install of 'patchwork' from a github remote, the SHA1 (fd7958ba) has not changed since last install.
## Use `force = TRUE` to force installation
library(patchwork)
tmax_tmin_p = ggplot(weather_df, aes(x = tmax, y = tmin, color = name)) +
geom_point(alpha = .5) +
theme(legend.position = "none")
prcp_dens_p = weather_df %>%
filter(prcp > 0) %>%
ggplot(aes(x = prcp, fill = name)) +
geom_density(alpha = .5) +
theme(legend.position = "none")
tmax_date_p = ggplot(weather_df, aes(x = date, y = tmax, color = name)) +
geom_point(alpha = .5) +
geom_smooth(se = FALSE) +
theme(legend.position = "bottom")
(tmax_tmin_p + prcp_dens_p) / tmax_date_p
## Warning: Removed 15 rows containing missing values (geom_point).
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## Warning: Removed 3 rows containing non-finite values (stat_smooth).
## Warning: Removed 3 rows containing missing values (geom_point).
Factors:
ggplot(weather_df, aes(x = name, y = tmax, fill = name)) +
geom_violin()
## Warning: Removed 3 rows containing non-finite values (stat_ydensity).
weather_df %>%
mutate(name = forcats::fct_relevel(name, c("Waikiki_HA", "CentralPark_NY", "Waterhole_WA"))) %>%
ggplot(aes(x = name, y = tmax)) +
geom_violin(aes(fill = name), color = "blue", alpha = .5) +
theme(legend.position = "bottom")
## Warning: Removed 3 rows containing non-finite values (stat_ydensity).
weather_df %>%
mutate(name = forcats::fct_reorder(name, tmax)) %>%
ggplot(aes(x = name, y = tmax)) +
geom_violin(aes(fill = name), color = "blue", alpha = .5) +
theme(legend.position = "bottom")
## Warning: Removed 3 rows containing non-finite values (stat_ydensity).
Advanced tidying:
weather_df %>%
select(name, tmax, tmin) %>%
gather(key = observation, value = temp, tmax:tmin) %>%
ggplot(aes(x = temp, fill = observation)) +
geom_density(alpha = .5) +
facet_grid(~name) +
viridis::scale_fill_viridis(discrete = TRUE)
## Warning: Removed 18 rows containing non-finite values (stat_density).